Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

750
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
750
Mean Absolute Deviation01:13

Mean Absolute Deviation

3.5K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
3.5K
Time-Series Graph00:54

Time-Series Graph

5.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.3K
Discrete Fourier Transform01:15

Discrete Fourier Transform

967
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
967
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

382
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
382
Divergence and Curl of Magnetic Field01:26

Divergence and Curl of Magnetic Field

4.1K
The magnetic field due to a volume current distribution given by the Biot–Savart Law can be expressed as follows:
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Measures of entropy and complexity in altered states of consciousness.

Cognitive neurodynamics·2018
Same author

Consciousness as a global property of brain dynamic activity.

Physical review. E·2018
Same author

Distinguishability notion based on Wootters statistical distance: Application to discrete maps.

Chaos (Woodbury, N.Y.)·2017
Same author

Statistical mechanics of consciousness: Maximization of information content of network is associated with conscious awareness.

Physical review. E·2016
Same author

Comment on "Quantum Kaniadakis entropy under projective measurement".

Physical review. E·2016
Same author

Effect of the microtubule-associated protein tau on dynamics of single-headed motor proteins KIF1A.

Physical review. E, Statistical, nonlinear, and soft matter physics·2014

Related Experiment Video

Updated: Feb 23, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

616

Detecting dynamical changes in time series by using the Jensen Shannon divergence.

D M Mateos1, L E Riveaud2, P W Lamberti2

  • 1Neuroscience and Mental Health Programme, Division of Neurology, Hospital for Sick Children, Institute of Medical Science and Department of Paediatrics, University of Toronto, Toronto, Ontario M5G 0A4, Canada.

Chaos (Woodbury, N.Y.)
|September 3, 2017
PubMed
Summary

This study introduces new methods for discretizing time series data to better distinguish between deterministic chaotic signals and random signals. These information theory tools effectively detect dynamical changes in both simulated and real-world data.

More Related Videos

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.2K
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.7K

Related Experiment Videos

Last Updated: Feb 23, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

616
Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.2K
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.7K

Area of Science:

  • Complex Systems Analysis
  • Signal Processing
  • Information Theory

Background:

  • Time series data often contain a mix of deterministic and random dynamics, making analysis challenging.
  • Distinguishing chaotic from random signals is difficult due to shared spectral and autocorrelation properties.
  • Continuous time series data require discretization for effective analysis.

Purpose of the Study:

  • To develop and present novel discretization schemes for time series analysis.
  • To introduce methods for detecting dynamical changes, specifically transitions between chaotic and random regimes.
  • To apply information theory tools for enhanced signal characterization.

Main Methods:

  • Development of various discretization schemes for time series.
  • Application of information theory principles for signal analysis.
  • Testing proposed methods on simulated and real-world datasets.

Main Results:

  • The proposed discretization schemes demonstrate high proficiency in detecting dynamical changes.
  • Successfully identified transitions between chaotic and random signal dynamics.
  • Effective application across both simulated and empirical time series data.

Conclusions:

  • The novel discretization and detection schemes provide a robust approach for analyzing complex time series.
  • Information theory offers powerful tools for differentiating signal dynamics.
  • The methods are effective for identifying regime shifts in natural and artificial signals.